Methods and apparatus for detecting heart failure event using patient chronic conditions

ABSTRACT

Devices and methods for detecting physiological target event such as events indicative of HF decompensation status are described. A medical device is configured to receive at least a first and a second chronic condition indictors of a patient, receive one or more physiologic signals from the patient, and generate a plurality of signal metrics when the first and the second chronic condition indicators meet their respective criterion. The medical device can detect the target event or condition using one or more patient-specific signal metrics selected from a group including both the first and the second set of the signal metrics. The medical device and the methods can be configured to detect an event indicative of HF decompensation.

CLAIM OF PRIORITY

This application claims the benefit of priority under 35 U.S.C. § 119(e)of U.S. Provisional Patent Application Ser. No. 61/825,172, filed on May20, 2013, which is herein incorporated by reference in its entirety.

TECHNICAL FIELD

This document relates generally to medical devices, and moreparticularly, to systems, devices and methods for detecting andmonitoring heart failure decompensation.

BACKGROUND

Congestive heart failure (CHF) is a major health problem and affectsover five million people in the United States alone. CHF patientstypically have enlarged heart with weakened cardiac muscles, resultingin poor cardiac output of blood. Elevated pulmonary vascular pressurescan cause fluid accumulation in the lungs over time. In many CHFpatients, fluid accumulation precedes episodes of heart failure (HF)decompensation. The HF decompensation can be characterized by pulmonaryor peripheral edema, reduced cardiac output, and symptoms such asfatigue, shortness of breath and the like.

Overview

Frequent monitoring of CHF patients and timely detection ofintrathoracic fluid accumulation or other events indicative of HFdecompensation status can help prevent worsening of HF in CHF patients,hence reducing cost associated with HE hospitalization.

Ambulatory medical devices can be used for monitoring HE patient anddetecting HF decompensation events. Examples of such ambulatory medicaldevices can include implantable medical devices (IMD), subcutaneousmedical devices, wearable medical devices or other external medicaldevices. The ambulatory or implantable medical devices can includephysiologic sensors which can be configured to sense electrical activityand mechanical function of the heart, and the medical device canoptionally deliver therapy such as electrical stimulation pulses to atarget area, such as to restore or improve the cardiac function. Some ofthese devices can provide diagnostic features, such as usingtransthoracic impedance or other sensor signals. For example, fluidaccumulation in the lungs can decrease the transthoracic impedance dueto the lower resistivity of the fluid than air in the lungs. The fluidaccumulation can also elevate ventricular filling pressure, resulting ina louder S3 heart sound. Additionally, fluid accumulation in the lungscan irritate the pulmonary system and leads to decrease in tidal volumeand increase in respiratory rate.

Desirable performance of a method or a device for detecting HFdecompensation can include one or more of a high sensitivity, a highspecificity, a high positive predictive value (PPV), or a highernegative predictive value (NPV). The sensitivity can represent apercentage of actual HF decompensation episodes that are correctlyrecognized by a detection method. The specificity can represent apercentage of actual non-HF decompensation episodes that are correctlyrecognized as non-HF decompensation events by the detection method. ThePPV can represent a percentage of the detected HF decompensationepisodes, as declared by the detection method, which are actual HFdecompensation events. The NPV can represent a percentage of thedetected non-HF decompensation episodes, as declared by the detectionmethod, which are actual non-HF decompensation events. A highsensitivity or a higher PPV can help ensure timely intervention to apatient with an impending HF decompensation episode, whereas a highspecificity or a high NPV can help avoid unnecessary intervention andadded healthcare cost due to false alarms. HF decompensation detectionmay be affected by a number of factors including the choice ofphysiologic sensors or physiologic signals. For example, a detectorusing a particular sensor signal may provide desirable accuracy in HFdecompensation event detection in one patient but less sensitive or lessspecific in another patient. Additionally, the performance of a detectorusing one type of sensor signal may change over time due to patient'sdisease progression or development of a new medical condition.Therefore, the present inventors have recognized that there remains aconsiderable need for improving HF decompensation events detection inCHF patients.

Various embodiments described herein can help improve the detection oftarget physiologic events such as events indicative of HF decompensationstatus. For example, a medical device (such as an implantable medicaldevice or a wearable medical device) can detect an HF decompensationevent, such as using sensor signals or signal metrics selected inaccordance with patient chronic condition indicators. A signal analyzercircuit can receive a patient status input which can include at leastfirst chronic condition indicator and a second chronic conditionindicator non-identical to the first chronic condition indicator. Thesignal analyzer can sense one or more physiologic signals from thepatient, and generate a plurality of signal metrics from the physiologicsignals. The signal metrics including a first set of one or more signalmetrics when the first chronic condition indicator meets at least onefirst specified criterion, and a second set of signal metrics when thesecond chronic condition indicator meets at least one second specifiedcriterion. The signal analyzer circuit can select one or morepatient-specific signal metrics from a group including both the firstand the second set of one or more signal metrics. The medical device caninclude an HF decompensation event detector circuit configured to detecta target event or condition of the patient using the selected one ormore patient-specific signal metrics.

A method can include detecting a target physiologic event or conditionsuch as an event indicative of HF decompensation status. The method caninclude receiving at least a first chronic condition indicator of thepatient and a second chronic condition indicator non-identical to thefirst chronic condition indicator, sensing one or more physiologicsignals of the patient, and generating a plurality of signal metricsfrom the one or more physiologic signals. The signal metrics can includea first set of one or more signal metrics when the first chroniccondition indicator meets at least one first specified criterion, and asecond set of signal metrics when the second chronic condition indicatormeets at least one second specified criterion. The method can includeselecting one or more patient-specific signal metrics from a groupincluding both the first and the second set of one or more signalmetrics, and applying a target event detection algorithm to the selectedone or more patient-specific signal metrics to detect a target event orcondition of the patient.

This Overview is an overview of some of the teachings of the presentapplication and not intended to be an exclusive or exhaustive treatmentof the present subject matter. Further details about the present subjectmatter are found in the detailed description and appended claims. Otheraspects of the invention will be apparent to persons skilled in the artupon reading and understanding the following detailed description andviewing the drawings that form a part thereof, each of which are not tobe taken in a limiting sense. The scope of the present invention isdefined by the appended claims and their legal equivalents.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments are illustrated by way of example in the figures ofthe accompanying drawings. Such embodiments are demonstrative and notintended to be exhaustive or exclusive embodiments of the presentsubject matter.

FIG. 1 illustrates an example of a cardiac rhythm management (CRM)system and portions of the environment in which the CRM system operates.

FIG. 2 illustrates an example of a patient chronic condition-basedtarget event detector.

FIG. 3 illustrates an example of a signal metrics generator circuit andthe patient-specific signal metrics selector circuit.

FIG. 4 illustrates an example of a target event or condition detectorcircuit.

FIG. 5 illustrates an example of a method for detecting a target eventfrom one or more physiological signals.

FIG. 6 illustrates an example of a method for generating one or moresignal metrics from a physiologic signal.

FIG. 7 illustrates an example of method for detecting a target eventfrom one or more physiological signals.

DETAILED DESCRIPTION

Disclosed herein are systems, devices, and methods for detecting one ormore physiologic target events or conditions. The events can includeearly precursors of an HF decompensation episode. That is, these eventscan occur well before the systematic manifestation of worsening of HF.Therefore, by detecting the precursor events, the present document canprovide a method and device to detecting an impending HF decompensationepisode. In particular, the methods and devices described herein can beapplicable to detecting events that can forecast an impending HFdecompensation episode. Such events can include accumulation ofintrathoracic fluid, changes in heart sounds, changes in breathingpatterns, changes in physiologic response to activity, or changes inposture. More generally, the systems, devices, and methods describedherein may be used to determine HF status and/or track HE′ progressionsuch as worsening and recovery from an event.

FIG. 1 illustrates an example of a Cardiac Rhythm Management (CRM)system 100 and portions of an environment in which the CRM system 100can operate. The CRM system 100 can include an ambulatory medicaldevice, such as an implantable medical device (IMD) 110 that can beelectrically coupled to a heart 105 such as through one or more leads108A-C, and an external system 120 that can communicate with the IMD 110such as via a communication link 103. The IMD 110 may include animplantable cardiac device such as a pacemaker, an implantablecardioverter-defibrillator (ICD), or a cardiac resynchronization therapydefibrillator (CRT-D). The IMD 110 can include one or more monitoring ortherapeutic devices such as a subcutaneously implanted device, awearable external device, a neural stimulator, a drug delivery device, abiological therapy device, or one or more other ambulatory medicaldevices. The IMD 110 may be coupled to, or may be substituted by amonitoring medical device such as a bedside or other external monitor.

As illustrated in FIG. 1, the MD 110 can include a hermetically sealedcan 112 that can house an electronic circuit that can sense aphysiological signal in the heart 105 and can deliver one or moretherapeutic electrical pulses to a target region, such as in the heart,such as through one or more leads 108A-C. The CRM system 100 can includeonly one lead such as 108B, or can include two leads such as 108A and108B.

The lead 108A can include a proximal end that can be configured to beconnected to IMD 110 and a distal end that can be configured to beplaced at a target location such as in the right atrium (RA) 131 of theheart 105. The lead 108A can have a first pacing-sensing electrode 141that can be located at or near its distal end, and a secondpacing-sensing electrode 142 that can be located at or near theelectrode 141. The electrodes 141 and 142 can be electrically connectedto the IMD 110 such as via separate conductors in the lead 108A, such asto allow for sensing of the right atrial activity and optional deliveryof atrial pacing pulses. The lead 108B can be a defibrillation lead thatcan include a proximal end that can be connected to IMD 110 and a distalend that can be placed at a target location such as in the rightventricle (RV) 132 of heart 105. The lead 108B can have a firstpacing-sensing electrode 152 that can be located at distal end, a secondpacing-sensing electrode 153 that can be located near the electrode 152,a first defibrillation coil electrode 154 that can be located near theelectrode 153, and a second defibrillation coil electrode 155 that canbe located at a distance from the distal end such as for superior venacava (SVC) placement. The electrodes 152 through 155 can be electricallyconnected to the IMD 110 such as via separate conductors in the lead108B. The electrodes 152 and 153 can allow for sensing of a ventricularelectrogram and can optionally allow delivery of one or more ventricularpacing pulses, and electrodes 154 and 155 can allow for delivery of oneor more ventricular cardioversion/defibrillation pulses. In an example,the lead 108B can include only three electrodes 152, 154 and 155. Theelectrodes 152 and 154 can be used for sensing or delivery of one ormore ventricular pacing pulses, and the electrodes 154 and 155 can beused for delivery of one or more ventricular cardioversion ordefibrillation pulses. The lead 108C can include a proximal end that canbe connected to the MID 110 and a distal end that can be configured tobe placed at a target location such as in a left ventricle (LV) 134 ofthe heart 105. The lead 108C may be implanted through the coronary sinus133 and may be placed in a coronary vein over the LV such as to allowfor delivery of one or more pacing pulses to the LV. The lead 108C caninclude an electrode 161 that can be located at a distal end of the lead108C and another electrode 162 that can be located near the electrode161. The electrodes 161 and 162 can be electrically connected to the IMD110 such as via separate conductors in the lead 108C such as to allowfor sensing of the LV electrogram and optionally allow delivery of oneor more resynchronization pacing pulses from the LV.

The IMD 110 can include an electronic circuit that can sense aphysiological signal. The physiological signal can include anelectrogram or a signal representing mechanical function of the heart105. The hermetically sealed can 112 may function as an electrode suchas for sensing or pulse delivery. For example, an electrode from one ormore of the leads 108A-C may be used together with the can 112 such asfor unipolar sensing of an electrogram or for delivering one or morepacing pulses. A defibrillation electrode from the lead 108B may be usedtogether with the can 112 such as for delivering one or morecardioversion/defibrillation pulses. In an example, the IMD 110 cansense impedance such as between electrodes located on one or more of theleads 108A-C or the can 112. The IMD 110 can be configured to injectcurrent between a pair of electrodes, sense the resultant voltagebetween the same or different pair of electrodes, and determineimpedance using Ohm's Law. The impedance can be sensed in a bipolarconfiguration in which the same pair of electrodes can be used forinjecting current and sensing voltage, a tripolar configuration in whichthe pair of electrodes for current injection and the pair of electrodesfor voltage sensing can share a common electrode, or tetrapolarconfiguration in which the electrodes used for current injection can bedistinct from the electrodes used for voltage sensing. In an example,the IMD 110 can be configured to inject current between an electrode onthe RV lead 108B and the can housing 112, and to sense the resultantvoltage between the same electrodes or between a different electrode onthe RV lead 108B and the can housing 112. A physiologic signal can besensed from one or more physiological sensors that can be integratedwithin the IMD 110. The IMD 110 can also be configured to sense aphysiological signal from one or more external physiologic sensors orone or more external electrodes that can be coupled to the IMD 110.Examples of the physiological signal can include one or more of heartrate, heart rate variability, intrathoracic impedance, arterialpressure, pulmonary artery pressure, RV pressure, LV coronary pressure,coronary blood temperature, blood oxygen saturation, one or more heartsounds, physical activity or exertion level, physiologic response toactivity, posture, respiration, body weight, or body temperature.

The arrangement and functions of these leads and electrodes aredescribed above by way of example and not by way of limitation.Depending on the need of the patient and the capability of theimplantable device, other arrangements and uses of these leads andelectrodes are.

As illustrated, the CRM system 100 can include a patient chroniccondition-based target event detector 113. The patient chroniccondition-based target event detector 113 can be configured to receivepatient chronic condition indicators and one or more physiologic signalsfrom the patient, and select one or more patient-specific signal metricsfrom the physiologic signals using the information of the patientchronic condition indicators. The physiologic signals from the patientcan be sensed using the electrodes on one or more of the leads 108A-C,or physiologic sensors deployed on or within the patient andcommunicated with the IMD 110. The patient chronic condition-basedtarget event detector 113 can detect a target event or condition of thepatient such as an HF decompensation event using the selectedpatient-specific signal metrics. The HF decompensation event can includeone or more early precursors of an HF decompensation episode, or anevent indicative of HF progression such as recovery or worsening of HFstatus. Examples of the patient chronic condition-based target eventdetection are described below, such as with reference to FIGS. 2-3.

The external system 120 can allow for programming of the MD 110 and canreceives information about one or more signals acquired by IMD 110, suchas can be received via a communication link 103. The external system 120can include a local external IMD programmer. The external system 120 caninclude a remote patient management system that can monitor patientstatus or adjust one or more therapies such as from a remote location.

The communication link 103 can include one or more of an inductivetelemetry link, a radio-frequency telemetry link, or a telecommunicationlink, such as an interact connection. The communication link 103 canprovide for data transmission between the IMD 110 and the externalsystem 120. The transmitted data can include, for example, real-timephysiological data acquired by the IMD 110, physiological data acquiredby and stored in the IMD 110, therapy history data or data indicatingIMD operational status stored in the IMD 110, one or more programminginstructions to the IMD 110 such as to configure the IMD 110 to performone or more actions that can include physiological data acquisition suchas using programmably specifiable sensing electrodes and configuration,device self-diagnostic test, or delivery of one or more therapies.

The patient chronic condition-based target event detector 113 may beimplemented in the external system 120. The external system 120 can beconfigured to perform HF decompensation event detection such as usingdata extracted from the 110 or data stored in a memory within theexternal system 120. Portions of the patient chronic condition-basedtarget event detector 113 may be distributed between the IMD 110 and theexternal system 120.

Portions of the IMD 110 or the external system 120 can be implementedusing hardware, software, or any combination of hardware and software.Portions of the MD 110 or the external system 120 may be implementedusing an application-specific circuit that can be constructed orconfigured to perform one or more particular functions, or can beimplemented using a general-purpose circuit that can be programmed orotherwise configured to perform one or more particular functions. Such ageneral-purpose circuit can include a microprocessor or a portionthereof, a microcontroller or a portion thereof, or a programmable logiccircuit, or a portion thereof. For example, a “comparator” can include,among other things, an electronic circuit comparator that can beconstructed to perform the specific function of a comparison between twosignals or the comparator can be implemented as a portion of ageneral-purpose circuit that can be driven by a code instructing aportion of the general-purpose circuit to perform a comparison betweenthe two signals. While described with reference to the IMD 110, the CRMsystem 100 could include subcutaneous medical device (e.g., subcutaneousICD, subcutaneous diagnostic device), wearable medical devices (e.g.,patch based sensing device), or other external medical devices.

FIG. 2 illustrates an example of a patient chronic condition-basedtarget event detector 200, which can be an embodiment of the patientchronic condition-based target event detector 113. The patient chroniccondition-based target event detector 200 can include one or more of asignal analyzer circuit 220, a target event detector circuit 230, acontroller 240, and an instruction receiver 250.

The signal analyzer circuit 220 can include a patient status input unit221, a signal receiver circuit 222, a signal metrics generator circuit223, and a patient-specific signal metrics selector circuit 224. Thepatient status input unit 221 can be configured to receive at least afirst chronic condition indicator and a second chronic conditionindicator of a patient. The second chronic condition indicator can benon-identical to the first chronic condition indicator. The patientchronic condition indicators can be descriptive of the patientcharacteristics or sustained medical status that do not change or slowlychange over a certain specified time, such as approximately at least sixmonths. The first and the second chronic condition indicators of thepatient can respectively include at least one of a chronic diseaseindicator of the patient, a prior medical procedure indicator of thepatient, or a demographic characteristic indicator of the patient.Example of the chronic disease indicator of the patient can include apatient's prior myocardial infarction (MI), ischemic or dilatedcardiomyopathy, valvular disease, renal disease, chronic obstructivepulmonary disease (COPD), peripheral vascular disease, cerebrovasculardisease, hypertension, hepatic disease, diabetes, asthma, anemia,depression, pulmonary hypertension, sleep disordered breathing,hyperlipidemia, among others. Examples of the prior medical procedurecan include prior coronary artery bypass graft (CABG) surgery, thoracicsurgery, valvular surgery, or other surgical interventions. Example ofdemographic characteristic indicator can include a patient's age, sex,race, height, ethnicity, blood type, past or current smoker, New Yorkheart association (NYHA) functional class, among others.

The patient status input unit 221 can be configured to receive at eastthe first and the second chronic condition indictors from a system user(e.g., a physician) such as via the instruction receiver 250. In anexample, the patient status input unit 221 can be configured to becoupled to a storage device, including an electronic medical record(EMR) system that can store at least the first and the second chroniccondition indicators of the patient. When receiving a command from asystem user such as via instruction receiver 250, the patient statusinput unit 221 can retrieve from the storage device such as the EMRsystem at least the first chronic condition indicator and the secondpatient chronic condition indicator non-identical to the first chroniccondition indicator. For example, the first chronic condition indictorincludes an indicator of the patient's prior thoracic surgery, and thesecond chronic condition indicator includes an indicator of patient'shaving a renal disease.

The chronic condition indicators received by the patient status inputunit 221 can include binary values indicating the presence or absence ofa specified condition. Additionally or alternatively, the chroniccondition indicators can include categorical classification orprogressive stage information of a demographic characteristic indicatoror a chronic disease indicator. For example, the chronic conditionindicators can include symptomatic or functional classification of heartfailure (e.g., NYHA classes I, II, III, and IV), diabetes mellitus type1 and type 2, chronic kidney disease (MD) of stage 1 through stage 5,among others. The chronic condition indicator can also include aplurality of value ranges of a physiologic parameter that indicatefunctional assessment of the patient's chronic condition. For example,the chronic conditions can include a plurality of value ranges ofglomerular filtration rate (GFR) indicating the progression of CKD, aplurality of value ranges of ejection fraction (EF) indicating theprogression of heart failure, a plurality of value ranges of bloodglucose indicating the progression of diabetes, a plurality of valueranges of apnea hypopnea index (AHI) indicating the progression of sleepdisordered breathing, a plurality of value ranges of forced expiratoryvolume in one second (FEV1) indicating the progression of chronicobstructive pulmonary diseases (COPD). The first and the second chroniccondition indicators can respectively include non-identical categoricalclasses from the same or different chronic condition indicators of thepatient.

The signal receiver circuit 222 can be configured to receive one or morephysiologic signals from the patient, such as for detecting a targetevent or condition. The signal receiver circuit 222 can be coupled toone or more of: one or more electrodes such as electrodes on one or moreof the leads 108A-C or the can 112; one or more physiologic sensors; orone or more patient monitors. The physiological signals can beindicative of the target event or condition such as HF decompensationstatus, including one or more electrograms such as sensed fromelectrodes on one or more of the leads 108A-C or the can 112, heartrate, heart rate variability, intrathoracic impedance, intracardiacimpedance, arterial pressure, pulmonary artery pressure, RV pressure, LVcoronary pressure, coronary blood temperature, blood oxygen saturation,one or more heart sounds, physiologic response to activity, apneahypopnea index, one or more respiration signals such as a respirationrate signal or a tidal volume signal. The signal receiver circuit 222can include one or more modules to perform signal conditioning (e.g.,signal amplification, digitization, or filtering) or parameterextraction from the sensed physiological signal. Examples of extractedsignal parameters can include: signal mean, median, or other centraltendency measures; a histogram of the signal intensity; one or moresignal trends over time; one or more signal morphological descriptors;or signal power spectral density at a specified frequency range. In anexample, the signal sensing circuit can sense two or more physiologicalsignals and can generate a composite signal parameter set such as usingthe two or more physiological signals.

The signal metrics generator circuit 223 can be configured to generate aplurality of signal metrics from the one or more physiologic signals. Asignal metric can include a sensor signal acquired by a physiologicsensor or from one or more electrodes on one or more of the leads 108A-Cor the can 112. A signal metric can also include a signal featureextracted from the sensor signal such as a statistical measure (e.g.,mean, median, standard deviation, variance, correlation, covariance, orother statistical value over a specified time segment), a morphologicalmeasure (e.g., peak, trough, slope, area under the curve), or a featureindicative of a physiologic activity (e.g., P wave and QRS complexes inan electrogram signal, or S1, S2, S3 or S4 components of a heart soundsignal, an inspiration phase and expiration phase of a respirationsignal). The signal metrics generated by the signal metrics generatorcircuit 223 can include at least a first set of one or more signalmetrics and a second set of signal metrics. The first set of signalmetrics can be generated in response to the first chronic conditionindicator meeting a first specified criterion, and the second set ofsignal metrics can be generated in response to the second chroniccondition indicator meeting a second specified criterion. For example,the first set of signal metrics can include a five-day average ofintracardiac impedance signal and a daily maximum respiration rate (MRR)signal when the patient has a chronic condition of renal disease, andthe second set of signal metrics can include a standard deviation of R-Rinterval signal (such as calculated from an electrogram or aintracardiac electrogram signal) and a five-day average of transthoracicimpedance signal when the patient has a chronic condition of diabetes.The signal metrics with respect to the chronic condition indicator canbe selected using the population-based information. Examples of thesignal metrics generator circuit 223 are described below, such as withreference to FIG. 3.

The patient-specific signal metric selector circuit 224 can beconfigured to select one or more patient-specific signal metrics fromthe plurality of signal metrics generated by the signal metricsgenerator circuit 223. The patient-specific signal metrics can be usedto detect the target event or condition such as an HF decompensationevent. The patient-specific signal metrics can include one or moresignal metrics selected from a group including both the first set of oneor more signal metrics and the second set of one or more signal metrics.In an example, if there is at least one signal metric shared by thefirst and the second set of the signal metrics (i.e., the intersectionof the first and the second sets of the signal metrics are not empty),the patient-specific signal metrics can include at least one signalmetric from an intersection of the first set and the second set ofsignal metrics. In another example, the patient-specific signal metricsselector circuit can include at least one signal metric from the firstset of signal metrics and at least one signal metric from the second setof signal metrics.

The target event or condition detector circuit 230 can receive inputfrom the patient-specific signal metrics selector circuit 224 and beconfigured to detect a target event or condition using the selected oneor more patient-specific signal metrics. A target event or condition caninclude a physiologic event indicative of an onset of a disease,worsening of a disease state, recovery of a disease state, a response toan intervention, or a change of a disease state. In an example, thetarget event or condition detector circuit 230 can detect the presenceof an event indicative of HF decompensation status. Examples of targetevent can also include a worsening HP, pulmonary edema, pulmonarycondition exacerbation such as COPD, asthma and pneumonia, or myocardialinfarction, among others.

The controller circuit 240 can control the operations of the signalanalyzer circuit 220 and the subcomponent circuits 221 through 224, thetarget event or condition detector circuit 230, and the data andinstruction flow between these components. The controller circuit 240can receive external programming input from the instruction receivercircuit 250 to control one or more of the receiving patient status,signal sensing, signal metrics generation, patient-specific signalmetrics selection, or the target event detection. Examples of theinstructions received by the instruction receiver 250 may include:selection of electrodes or sensors used for sensing physiologic signals,selection of chronic conditions of the patient, or configuration of thetarget event and condition detector 230. The instruction receivercircuit 250 can include a user interface configured to presentprogramming options to the user and receive user's programming input.The instruction receiver circuit 250 can be coupled to the patientstatus input unit 221 to receive the patient chronic conditionindicators such as via the user interface. In an example, at least aportion of the instruction receiver circuit 250, such as the userinterface, can be implemented in the external system 120.

FIG. 3 illustrates an example of the signal metrics generator circuit223 and the patient-specific signal metrics selector circuit 224, aspart of the patient chronic condition-based target event detector 200.The signal metrics generator circuit 223 can include one or more of asignal metrics extractor 310, a signal metrics performance analyzercircuit 320, and a signal metrics performance comparator 330. Thepatient-specific signal metrics selector circuit 224 can include achronic-condition indexed signal metrics performance comparator 341 anda chronic-condition indexed signal metrics selector 342.

The signal metrics extractor 310 can receive one or more processedphysiologic signals sensed from the patient and extract signal featuresfrom the physiologic signals. The extracted signal metrics can include astatistical signal feature, a morphological signal feature, or a signalfeature indicative of a physiologic activity. Examples of the extractedsignal features can include mean, median, standard deviation, variance,correlation, covariance, or other statistical value over a specifiedtime segment; peak, trough, slope, area under the curve of a sensorsignal, P wave and QRS complexes in an electrogram signal, or S1, S2, S3or S4 components of a heart sound signal, inspiration and expirationphase of a respiration signal, among others.

The signal metrics extractor 310 can extract signal features when apatient chronic condition indicator such as that received by the patientstatus input unit 221 meets a specified condition. For example, when apatient chronic condition of having a chronic kidney disease is receivedby the patient status input unit 221, the signal metrics extractor 310can extract signal features including daily maximum respiration rate(MRR), transthoracic impedance, and daily S3 heart sound amplitude. Whentwo or more patient chronic condition indicators are received, thesignal metrics extractor 310 can extract signal features with respect toeach of the patient chronic condition indicator. The association betweena chronic condition indicator and the respective signal metrics can bestored in a machine-readable medium such as a memory device, associationcan be created in a form of searchable data structure such as a lookuptable or association map to facilitate automatic selection of signalmetrics for any given chronic condition indicator. The data structurecan contain a plurality of chronic condition indicators or categoricalclassifications of a chronic condition indicator, each of which can beassociated to the respective one or more signal metrics.

The signal metrics performance analyzer circuit 320 can be configured togenerate, for one or more of the signal metrics from the signal metricsextractor 310, respective performance measures indicative of reliabilityor accuracy of detecting a target event or condition. As illustrated inFIG. 3, the signal metrics performance analyzer circuit 320 can includeat least one of a signal sensitivity calculator circuit 321, a signalspecificity calculator circuit 322, or a signal quality calculatorcircuit 323. Each of the calculator circuits 321 through 323 alone or inany combination can be used to evaluate the performance of the one ormore signal metrics provided by the signal metrics extractor 310.

The sensitivity calculator circuit 321 can be configured to determinethe sensitivity of a signal metric in response to a physiologic changeassociated with the target event or condition, such as using comparisonof measurements of the signal metric at two non-identical states. Thefirst and the second states can include non-identical temporalinformation. In an example, the sensitivity calculator circuit 321 cancalculate for a signal metric (X) a relative change (such as adifference ΔX) between the signal metric at a first state (X_(S1)) andthe signal metric at the second state (X_(S2)), that is,ΔX=X_(S1)−X_(S2). The relative change can include a rate of change(ΔX/Δt) of the signal metric over the duration between the first and thesecond state, that is, ΔX/Δt=(X_(S1)−X_(S2))/(T_(S1)−T_(S2)), whereT_(S1) and T_(S2) respectively represents occurrence time of the firstand the second states. In an example, T_(S1) can be approximately 14-28days prior to patient's developing a target event such as andecompensation event, and T_(S2) can be a time preceding T_(S1) byspecified time duration of approximately 1-6 months or approximately 1-3months. The second state can be a baseline state representing thehistorical trend of the signal metric when the patient does not developthe target event. The signal metric at the baseline state can becomputed using a linear or nonlinear combination of a plurality ofhistorical measurements of the signal metric. The relative change in thesignal metric from the first state (such as the state prior to an BFdecompensation event) to the second state (such as a state preceding thefirst state or a baseline state) thus can indicate predictive content inthe signal metric in response to the progression of a target event. Asignal metric with high sensitivity preserves high predictive content indetecting a target event such as an HF decompensation event. In anexample, the sensitivity calculator circuit 321 can calculate astatistical significance of relative change ΔX or ΔX/X_(S2). Thesignificance can be computed as a p-value obtained by fitting relativechange data from a group of patients to a statistical model. Thesensitivity of a signal metric can be computed using both the p-value asdiscussed above and the relative change ΔX or ΔX/X_(S2). A signal metrichaving a large and significant change, such as large ΔX or ΔX/X_(S2) andsmall p value, is more desirable for detecting a target event such as aHF decompensation event. In an example, a sensitivity score can becomputed as a product of a relative change ΔX/X_(S2) and a negativelogarithm of the p-value, i.e., −log(p-value)*ΔX/X_(S2). A highersensitivity score preserves high predictive content in detecting thetarget event.

The specificity calculator circuit 322 can be configured to determinethe specificity of a signal metric in response to a physiologic ornon-physiologic change not associated with the target event orcondition. For example, in detecting the target event of HFdecompensation event, confounding events such as noise, inference,patient activity, lead fracture, lead revision, change of pacingconfiguration, or a replacement of the device are not physiologicchanges associated with an impending HF decompensation event. Thespecificity can characterize the accuracy of the signal metric inrecognizing the confounding events as non-target event. The specificitycalculator circuit 322 can determine the specificity using comparison ofmeasurements of the signal metric at two non-identical states. The firstand the second states can include non-identical temporal information.The specificity calculator circuit 322 can calculate a relative changein a signal metric (X) from a first state to a second state. The firststate can occur at a time such as approximately 14-28 days prior topatient's developing a target event such as an HP decompensation event.The second state can occur at a time preceding the first state by atleast specified time duration, such as approximately 1-6 months orapproximately 1-3 months. The second state can be a baseline staterepresenting the historical trend of the signal metric. The relativechange in the signal metric from the first state (such as the stateprior to an IV decompensation event) to the second state (such as astate preceding the first state or a baseline state) thus can indicateresponse of the signal metric in the absence of the target event. Asignal metric with high specificity therefore reduces the rate of falsedetection of non-target event as a target event.

The signal quality calculator circuit 323 can be configured to determinethe signal quality of the signal metric. Signal quality can includesignal strength, signal variability, or signal-to-noise ratio, amongothers. Examples of the signal variability can include range,inter-quartile range, standard deviation, variance, sample variance, orother first-order, second-order, or higher-order statistics representingthe degree of variation. For example, in determining the quality of asignal metric of S1 heart sound intensity, the signal quality calculatorcircuit 323 can perform a plurality of measurements of the S1 heartsound intensity such as from a plurality of cardiac cycles during aspecified period of time. The signal quality calculator circuit 323 candetermine the variability of the S1 intensity by computing a variance ofthe plurality of measurements of the S1 intensity. A high signalquality, such as indicated by one or more of a high signal-to-noiseratio, a high signal strength, or a low signal variability, is desirablefor detecting the target event.

The signal sensitivity calculator circuit 321, the signal specificitycalculator circuit 322, or the signal quality calculator circuit 323 candetermine respective signal performance measures (e.g., signalsensitivity, specificity, or quality) using population-based statistics.For example, the signal metric of daily maximum respiration rate (MRR)can be measured in a cohort of patients with the chronic condition (suchas renal disease) identical or similar to the present patient.Statistics such as the signal sensitivity, signal specificity, or signalquality with regard to the population-based daily MRR can be determinedfrom the population-based data. These statistical data can be retrievedfrom a database or otherwise provided to the signal metrics performanceanalyzer circuit 320 and used by one or more of the signal sensitivitycalculator circuit 321, the signal specificity calculator circuit 322,or the signal quality calculator circuit 323. In an example, the signalmetrics performance analyzer circuit 320 can determine respective signalperformance measures using both the population-based statistics (e.g.,population-based sensitivity of daily MRR) and the performance measureof the patient (e.g., sensitivity of daily MRR).

The signal metrics performance comparator 330 can be coupled to thesignal metrics performance analyzer circuit 320, and configured toselect one or more signal metrics from a plurality of signal metricsbased at least in part on a comparison of the performance measuresincluding the signal sensitivity, the signal specificity, or the signalquality, or any combination thereof. In an example, the signal metricsperformance comparator 330 can compute a composite performance score fora signal metric using a linear combination or nonlinear combination ofone or more of performance measures. The composite performance score canthen be compared to a specified threshold to determine whether thesignal metric can be selected. For example, a composite performancescore (F_(MRR)) for the signal metric of daily maximum respiration rate(NCR) can be computed as F_(MRR)=a*Ss+b*Sp+c*Q, where a, b, c are scalarweights to respective performance measures sensitivity (Ss), specificity(Sp), and quality (Q). The signal metrics performance comparator 330 canselect daily MRR for detecting the target event of HF decompensationevent if F_(MRR) exceeds a specified threshold, e.g.,F_(MRR)>F_(MRR−TH). In another example, the signal metrics performancecomparator 330 can select the signal metric when one or more performancemeasures meet specified criteria. For example, the signal metricsperformance comparator 330 can select daily MRR for detecting HFdecompensation event if sensitivity, specificity, or quality eachexceeds respective threshold, e.g., Ss>Ss_(−TH), Sp>Sp_(−TH), orQ>Q_(TH).

When the signal metrics extractor 310 generates and provides more thanone signal metric to the performance analyzer circuit 320, the signalmetrics performance comparator 330 can organize the signal metrics suchas prioritizing the signal metrics. The signal metrics performancecomparator 330 can prioritize the signal metrics based at least in parton their respective performance measures. For example, corresponding toa chronic condition indicator of patient having diabetes, the signalmetrics extractor 310 can generate a set of signal metrics includingstandard deviation of average normal RR interval (SDANN), 24-hour maxintracardiac impedance (Zmax), daily maximum of respiration rate (MRR),and daily maximum of tidal volume (TV). The signal metrics performanceanalyzer circuit 320 can compute composite performance score (F) foreach of the signal metrics. If the comparison of the performancemeasures at the signal metrics performance comparator 330 indicates thatF_(MRR)≥F_(TV)≥F_(Zmax)≤F_(SDANN), then the prioritized signal metricscan be in the order of (MRR, TV, Zmax, SDANN).

The patient-specific signal metrics selector circuit 224 can be coupledto the signal metrics generator circuit 223 and receive therein at leasta first set of one or more signal metrics corresponding to the firstchronic condition indicator and a second set of signal metricscorresponding to the second chronic condition indicator. Thechronic-condition indexed signal metrics performance comparator 341 cancompare the signal metrics in the first set to the signal metrics in thesecond set, such as to identify one or more common signal metrics sharedby the first and the second sets of the signal metrics. Thechronic-condition indexed signal metrics performance comparator 341 canalso compare the performance measures such as the signal sensitivity,signal specificity, or signal quality of the signal metrics in the firstand the second sets.

The chronic-condition indexed signal metrics selector 342 can beconfigured to select from the plurality of signal metrics one or morepatient-specific signal metrics selected from a group including both thefirst set of one or more signal metrics and the second set of one ormore signal metrics. In an example, the chronic-condition indexed signalmetrics selector 342 can select at least one signal metric from anintersection of the first set and the second set of signal metrics,where the intersection includes at least one common signal metric sharedby both the first and the second set of signal metrics. Thechronic-condition indexed signal metrics selector 342 can select atleast one signal metric from the first set of signal metrics and atleast one signal metric from the second set of signal metrics. Forexample, the chronic-condition indexed signal metrics selector 342 canuse the prioritized signal metrics such as provided by the signalmetrics performance comparator 330, and select a signal metric with thehighest composite performance from the first set, and a signal metricwith the highest composite performance score from the second set. Otherapproaches of selecting patients-specific signal metrics from the firstand the second sets based at least in part on the performance measureshave also been contemplated.

FIG. 4 illustrates an example of target event or condition detectorcircuit 400, which can be an embodiment of the target event or conditiondetector circuit 230. The target event or condition detector circuit 400can receive the chronic-condition indexed signal metrics from thepatient-specific signal metrics selector circuit 224, and detects thetarget event from the received signal metrics. The target event orcondition detector circuit 400 can include a target event detector 401,a target event report generator 402, and a signal metrics selection andperformance report generator 403.

The target event detector 401 can detect the target event from thechronic-condition indexed signal metrics. In an example, the targetevent can be an event indicative of HF decompensation status, and thetarget event detector 401 can be configured to compute an HFdecompensation index such as using the chronic-condition indexed signalmetrics, and determine whether the HF decompensation index meets aspecified criterion such as by comparing the HF decompensation index toa threshold value. Examples of target HF decompensation event detectionusing the chronic-condition indexed signal metrics are discussed below,such as with reference to FIGS. 5-7.

The target event report generator 402 can generate a report to inform,warn, or alert the user the detected target event. The report caninclude recommendations such as confirmative testing, diagnosis, ortreatment options. The report can include one or more formats of mediaincluding, for example, a textual or graphical message, a sound, animage, or a combination thereof. In an example, the target event reportgenerator 402 can be coupled to the instruction receiver circuit 250 andthe report can be presented to the user via an interactive userinterface on the instruction receiver circuit 250. The target eventreport generator 402 can be coupled to the external device 120, and beconfigured to present to the user the detected target event via theexternal device 120.

The signal metrics selection and performance report generator 403 cangenerate, and present to the system user, one or more of a reportincluding the chronic condition indicators such as received from thepatient status input unit 221, signal metrics and the respectiveperformance measures such as generated by the signal metrics generatorcircuit 223, or the chronic-condition indexed signal metrics such asgenerated by the patient-specific signal metrics selector circuit 224.The signal metrics selection and performance report generator 403 can becoupled to the external device 120 or the instruction receiver circuit250, and be configured to present the signal metrics information to theuser therein. The user input can include confirmation, storage, or otherprogramming instructions to operate on the patient-specific signalmetrics or patient chronic condition indicators.

FIG. 5 illustrates an example of a method 500 for detecting a targetevent from one or more physiological signals. The method 500 can beimplemented and operate in an ambulatory medical device or in a remotepatient management system. In an example, the method 500 can beperformed by the patient chronic condition-based target event detectorcircuit 113 implemented in IMD 110, or the external device 120 which canbe in communication with the IMD 110.

At 501, at least two chronic condition indicators of a patient can bereceived. The patient chronic condition indicators can be descriptive ofthe patient characteristics or sustained medical status that do notchange or slowly change over a certain specified time, such asapproximately at least six months. The at least two chronic conditionindicators, including a first and a second chronic condition indicatorof the patient, can be non-identical. For example, the first and thesecond chronic condition indicators can respectively include at leastone of a chronic disease indicator of the patient e.g., prior MI,ischemic or dilated cardiomyopathy, valvular disease, renal disease,COPD), a prior medical procedure indicator of the patient (e.g., CABG,thoracic surgery), or a demographic characteristic indicator of thepatient (e.g., age, sex, race, height, NYHA classifications). Thechronic condition indicators can include binary values indicating thepresence or absence of a specified condition, a plurality of categoricalclassifications or progressive stages of a particular chronic condition,or a plurality of value ranges of a physiologic parameter.

At 502, one or more physiological signals can be sensed from thepatient. The physiological signal may represent electrical or mechanicalactivities in the body. Examples of the physiological signal include:heart rate, heart rate variation, conduction times, arrhythmias,intrathoracic impedance, intracardiac impedance, arterial pressure,pulmonary artery pressure, RV pressure, LV coronary pressure, heartsounds, respiration signals including respiration rate or tidal volume,posture, activity, physiologic response to activity; coronary bloodtemperature, blood oxygen saturation, electrolyte concentrations, orother measures descriptive of the patient's physiology. Thephysiological signal can be received from a signal sensing circuitcoupled to the electrodes on one or more of the leads such as 108A-C, orimplanted or external physiologic sensors associated with an ambulatorymedical device. The physiologic signal can also be received from asignal memory where the physiological data are stored.

At 503, a plurality of signal metrics can be generated from the one ormore physiologic signals. A signal metric can include signal featureextracted from the one or more physiologic signals, such as astatistical measure, a morphologic measure, or a portion of thephysiologic signal indicative of a physiologic activity. Examples of thesignal metrics can include: mean, median, or other central tendency of aportion of a physiologic signal; variance, standard deviation, or othersecond-order or higher-order statistical measure of a portion of aphysiologic signal; peak, trough, slope or other morphological featuresof a portion of a physiologic signal; P wave and QRS complexes in anelectrogram signal; S1, S2, S3 or S4 components of a heart sound signal;inspiration phase and expiration phase of a respiration signal.

The generated signal metrics can include a first set of one or moresignal metrics when the first chronic condition indicator meets a firstspecified criterion, and a second set of one or more signal metrics whenthe second chronic condition indicator meets a second specifiedcriterion. Since the first and the second chronic condition indicatorscan be non-identical to each other, the first set of the signal metricsmay not be identical to the second set of signal metrics. In an example,the first chronic condition indicator is CKD and the second chroniccondition indicator is diabetes. When the first chronic conditionindicator meets the criterion of “CKD of stage 3 or above”, the firstset of signal metrics (M_(CKD)) can include: M_(CKD)={daily maximumrespiration rate signal (MRR), average of intracardiac impedance signal(AvgZ1), 53 heart sound amplitude (S3Amp)}. When the second chroniccondition indicator meets the criterion of “patient having diabeteswithin the past 5 years”, the second set of signal metrics(M_(Diabetes)) can include: M_(Diabetes)={standard deviation of averageR-R intervals (SDANN), average of intracardiac impedance signal(AvgZ1)}. Examples of the selection of signal metrics in accordance withthe patient chronic condition indicator is discussed below, such as withreference to FIG. 6.

At 504, one or more patient-specific signal metrics can be selected fromthe plurality of signal metrics such as generated at 503. Thepatient-specific signal metrics can includes at least one signal metricfrom an intersection of the first set of signal metrics corresponding tothe first chronic condition indicator and the second set of signalmetrics corresponding to the second chronic condition indicator. In anexample where the first set of signal metrics corresponding to CKDincludes M_(CKD)={MRR, AvgZ1, S3 Amp} and the second set of signalmetrics includes M_(Diabetes)={SDANN, AvgZ1}, the intersection of thetwo sets M_(CKD) and M_(Diabetes), namely AvgZ1, can be selected as thepatient-specific signal metrics, that is, M_(P)={AvgZ1}. In anotherexample, the patient-specific signal metrics can include at least onesignal metric from the first set of signal metrics and at least onesignal metric from the second set of signal metrics. Using the exampleof M_(CKD) and M_(Diabetes) as discussed above, the patient-specificsignal metrics can be selected as M_(P)={MRR, SDANN}. The selection ofthe signal metric from each set to be a patient-specific signal metricscan be performed using performance measure of respective signal metricsin each set. Examples of the performance measure of the signal metricsis discussed below, such as with reference to FIG. 6.

At 505, a target event or condition detection algorithm can be appliedto the selected patient-specific signal metrics, such as that determinedat 504, to detect a target event. The method of target event detectioncan be performed by the target event detector circuit 400.

The target event detection algorithm can include an algorithm fordetecting an event indicative of HF decompensation status of thepatient. The HF decompensation event detection algorithm includescomputing a decompensation index such as using the patient-specificsignal metrics. The decompensation index can be a quantitative parameterindicating the presence or severity of a physiologic conditionprecipitating an HF decompensation episode, such as excessiveintrathoracic fluid accumulation, increased heart sounds, increasedheart rate, increased respiratory rate, decreased tidal volume, orreduction in activity. In an example, an HF decompensation event isdetected if the decompensation index is greater than a threshold.Examples of the target event detection algorithm is discussed below,such as with reference to FIG. 7.

FIG. 6 illustrates an example of a method 600 for generating a pluralityof signal metrics from the one or more physiologic signals. The method600 can be an example of 503. The method can take the input of aplurality of chronic condition indicators including at least a first anda second chronic condition indicators from a patient and one or morephysiologic signals sensed from the patient such as using one or morephysiologic sensors, and generate a set of signal metrics correspondingto each respective chronic condition indicator. The method 600 can alsogenerate performance measure of each signal metric, which can be used toselect one or more patient-specific signal metrics such as at 504. In anexample, the method 600 can be performed by the signal metrics generatorcircuit 223 as illustrated in FIG. 2.

At 601, for each received chronic condition indicator, a respective setof candidate signal metrics can be generated when the chronic conditionindicator meets a specified criterion, such as being categorized as aspecified class of status, stage of disease, or a value range of aphysiologic parameter. The candidate signal metrics associated with eachchronic condition indicator, including the number, type, or methods ofgenerating the candidate signal metrics, can be pre-determined such asusing the population-based data. For example, for patient with CKD ofstages 3 or above, various signal metrics can be evaluated in a cohortof patients with the similar chronic condition (e.g., CKD of stage 3 orabove) and another cohort of patients absent of similar chroniccondition. If a signal metric (e.g. daily maximum respiration rate, orMRR) shows a desirable level of difference between the two cohorts ofpatients, then the signal metric can be determined as a candidate signalmetric associated with the chronic condition of CKD.

At 602, a signal sensitivity measure can be generated for each candidatesignal metric. A sensitivity measure can be indicative of the ability ofa signal metric in predicting a progression of a target event orcondition. The sensitivity measure can be computed as a relative changeof the respective signal metric from a first state to a second state. Inan example, the second state includes temporal information non-identicalto that of the first state. For example, in detecting an IVdecompensation event, the first state can occur at a time prior topatient's developing a target event such as an HF decompensation event,and the second state can occur at a time preceding the first state by atleast specified time duration, such as approximately 1-6 months orapproximately 1-3 months. In another example, the second state can be abaseline state computed using a plurality of historical measurements ofthe signal metric. The sensitivity measure can include a false negativerate of detecting a target event or condition. A low false negative ratecan correspond to a high sensitivity measure. The sensitivity measurecan also include a positive predictive value (PPV) which representslikelihood of correctly recognizing a target event or condition. Asignal metric with high sensitivity preserves predictive content indetecting a target event such as an HF decompensation event.

The computed signal sensitivity measure can be used to determine aperformance measure for the respective signal metric at 603. Theperformance measure can indicate how welt the signal metric can predictthe progression or detect the occurrence of a target event or condition.In an example, the performance measure can include the sensitivitymeasure of the signal metric and the population-based statistics of thesignal metrics. While the sensitivity measure can include“intra-patient” relative change of the signal metrics between two stateswith non-identical temporal information, the population-based statisticscan include “inter-patient” consistency of the signal metric inpreserving the predictive content in detecting a target event. Forexample, population-based statistics, such as average, variance, orstatistical distribution of the sensitivity measure of daily MRR acrossa cohort of patients with similar type of chronic condition e.g., CKD ofstage 3 and above), can be retrieved from the patient database, andfurther be used in determining the performance measure.

The performance measure for the respective signal metric can bedetermined using other parameters either in conjunction with or as analternative to the sensitivity measure of the respective signal metric.In one example, the performance measure can include a specificitymeasure of a signal metric in response to a physiologic ornon-physiologic change not associated with the target event orcondition. The specificity measure can be calculated as a relativechange in a signal metric from a first state such as the state prior toan HF decompensation event) to the second state (such as a statepreceding the first state or a baseline state) in the absence of thetarget event. The specificity measure can include a false positive rateof detecting a target event or condition. A low false positive rate cancorrespond to a high specificity measure. The specificity measure canalso include a negative predictive value (NPV) which representslikelihood of correctly recognizing a non-target event or condition. Inanother example, the performance measure can include a signal qualitymeasure such as signal strength, signal variability, or signal-to-noiseratio. In various examples, population-based statistics of thespecificity measure or of the signal quality measure, such as centraltendency or statistical distribution computed using data of a cohort ofpatient with similar type of chronic condition, can be retrieved from adatabase and used in determining the performance measure.

At 604, candidate signal metrics with respect to each chronic conditionindicator input can be compared and prioritized using at least theirrespective performance measures. In an example, for each candidatesignal metric, a composite performance score can be computed such as alinear combination or nonlinear combination of one or more of thesensitivity measure, the specificity measure, or the signal qualitymeasure. The candidate signal metrics with respect to each chroniccondition indicator can be prioritized such as being organized in adescending order at least based on the their respective compositeperformance scores. The prioritized candidate signal metrics and therespective composite performance scores can be to select one or morepatient-specific signal metrics such as at 504.

FIG. 7 illustrates an example of a method 700 for detecting a targetevent from one or more physiological signals such as using informationof the patient chronic condition indicators. The method 700 can be anembodiment of the method 500. In an example, the method 700 can beperformed by the target event detector 200.

At 701, one or more physiological signals of a patient can be received.The physiologic signals may be received from a signal sensing circuitcoupled to electrodes or external or implanted physiologic sensorsassociated with an ambulatory medical device or a physiological monitor,or from a signal memory.

At 702, a first chronic condition indicator of the patient can bereceived such as from a system user (e.g., a physician) via a userinterface on a transceiver in communication with the ambulatory medicaldevice. The chronic condition indicator can include a chronic diseaseindicator of the patient, a prior medical procedure indicator of thepatient, a demographic characteristic indicator of the patient, or anyother condition descriptive of the patient characteristics or sustainedmedical status that do not change or slowly change over a certainspecified time, such as approximately at least six months. The firstchronic condition indicator can be compared to a specified criterion at703, such as determining if the first chronic condition indicator fallsunder a categorical class or a progressive stage of a chronic disease.When the first chronic condition indicator meets the specifiedcriterion, a first set of signal metrics can be generated using the oneor more physiologic signals at 703. The signal metrics can be generatedaccording to specified instructions and methods stored in the ambulatorymedical device. For each signal metric a respective performance measurecan be computed, which includes a sensitivity, a specificity, or asignal quality measure such as the signal-to-noise ration of the signalmetric.

At 704, a second chronic condition indicator of the patient can bereceived. The second chronic condition indicator can be non-identical tothe first chronic condition indicator. A second set of signal metricscan be generated at 705 using a similar approach of signal metricsgeneration for the first chronic condition indicator at 703. Because thesecond chronic condition indicator is not identical to the first chroniccondition indicator, the second set of signal metrics can benon-identical to the first set of signal metrics.

At 706, one or more patient-specific signal metrics can be selected froma group including both the first and second sets of signal metrics. Inan example, if there is at least one signal metric shared by the firstand the second set of the signal metrics, the patient-specific signalmetrics can include at least one signal metric from an intersection ofthe first set and the second set of signal metrics. Alternatively oradditionally, the patient-specific signal metrics can include at leastone signal metric from the first set of signal metrics and at least onesignal metric from the second set of signal metrics. For example, thesignal metrics with respect to each chronic condition indicator can beorganized according to their respective performance measure such as thesensitivity to the progression of target event. The patient-specificsignal metrics can include the signal metric with the highestperformance score from the first set and the signal metric with thehighest performance score from the second set.

At 707, a target event risk index can be generated using the one or morepatient-specific signal metrics. The target event risk index can be aquantity indicating the presence or severity of a physiologic conditionprecipitating the target event, such as excessive intrathoracic fluidaccumulation in a target event indicative of HF decompensation status.

For each patient-specific signal metric selected at 706, a correspondingrisk index can be computed such as an accumulated deviation of thesignal metric intensity from a reference value over time. The intensityof the signal metric can include a central tendency measure (e.g. a meanor a median) of the signal metric over a specified time such as 3-10days. The reference value can be a moving average of the signal metric,a low-pass or band-pass filtered signal metric with pre-determinedfilter coefficients, or other measures representing the trend of theintensity of the signal metric. In some examples, the deviation of thesignal metric intensity from a reference value can be accumulated onlyif a certain criterion is met, such as the difference between theintensity of the signal metric and the decompensation index beinggreater than a specified threshold. Other examples of decompensationindex can include the cumulative sum in detecting persistent shifts inthe trended signal found in Brockway et al., U.S. Pat. No. 7,761,158,entitled “Detection of Heart Failure Decompensation Based on CumulativeChanges in Sensor Signals,” filed Dec. 20, 2005, which is incorporatedherein by reference in its entirety.

The target event risk index at 707 can be a linear or nonlinearcombination of the individual risk index associated with eachpatient-specific signal metric. In an example, the target event riskindex can be computed as a sum of individual risk index each scaled by aweight factor. The weight factor can be proportional to the performancemeasure of the respective signal metric. For example, if at 706 twopatient-specific signal metrics, namely daily MRR and SDANN, areselected with respective performance measure F_(MRR) and F_(SDANN), andthe respective individual risk index is R_(MRR) and R_(SDANN), then thetarget event risk at 707 can be determined asR=R_(MRR)*F_(MRR)/(F_(MRR+) F_(SDANN))+R_(SDANN)*F_(SDANN)/(F_(MRR+)F_(SDANN)). The target event risk index can also be determined as aparametric or non-parametric model using the individual risk index, suchas decision trees, neural network, Bayesian network, among other machinelearning methods. In an example, the target event risk index can becomputed using a probability model p(D)=1/(1+exp(x)), where p(D)represents a probability of the target event such as a HF decompensation(D) event, and x represents a linear or nonlinear combination ofindividual risk indices, or a linear or nonlinear combination ofmeasurements of the selected patient-specific signal metrics.

At 708, the target event risk index is checked against a specifiedcriterion, such as compared to a specified threshold value, to determineif a target event is detected. A target event is deemed detected if thetarget risk index meets the specified criterion, and at 710 a report isgenerated to inform the user the detected target event. The report caninclude a textual or graphical message, a sound, an image, or anycombination thereof.

If the target event risk index does not meet the specified criterion,then at 709 a decision is made as to whether a new set ofpatient-specific signal metrics is to be used for computing a targetevent risk index. The new set of the patient-specific signal metrics canbe different than the patient-specific signal metrics used at 706. Forexample, if at 706 the patient-specific signal metrics include only theintersection between the first set of signal metrics such as generatedat 703 and the second set of signal metrics such as generated at 705,then at 709 the patient-specific signal metrics can include the signalmetric with the highest performance score from the first set and thesignal metric with the highest performance score from the second set. Inanother example, if at 706 the patient-specific signal metrics includethe signals metrics with the highest performance score from the firstand the second sets, then at 709, the patient-specific signal metricscan include the signals metrics with the second highest performancescore from the first and the second sets. The decision at 709 can bereceived from a system user, or automatically executed in response tothe target event risk index as determined at 708 fails to meet thecriterion (e.g., falling below the detection threshold) by a narrowmargin. If a different set of patient-specific signal metrics is decidedto be used, then a new target event risk index can be generated at 707;otherwise, no target event is deemed detected, and the detection cancontinue with receiving the physiological signals at 701.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments in which theinvention can be practiced. These embodiments are also referred toherein as “examples.” Such examples can include elements in addition tothose shown or described. However, the present inventors alsocontemplate examples in which only those elements shown or described areprovided. Moreover, the present inventors also contemplate examplesusing any combination or permutation of those elements shown ordescribed (or one or more aspects thereof), either with respect to aparticular example (or one or more aspects thereof or with respect toother examples (or one or more aspects thereof) shown or describedherein.

In the event of inconsistent usages between this document and anydocuments so incorporated by reference, the usage in this documentcontrols.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In this document, the terms “including” and “inwhich” are used as the plain-English equivalents of the respective terms“comprising” and “wherein.” Also, in the following claims, the terms“including” and “comprising” are open-ended, that is, a system, device,article, composition, formulation, or process that includes elements inaddition to those listed after such a term in a claim are still deemedto fall within the scope of that claim. Moreover, in the followingclaims, the terms “first,” “second,” and “third,” etc. are used merelyas labels, and are not intended to impose numerical requirements ontheir objects.

Method examples described herein can be machine or computer-implementedat least in part. Some examples can include a computer-readable mediumor machine-readable medium encoded with instructions operable toconfigure an electronic device to perform methods as described in theabove examples. An implementation of such methods can include code, suchas microcode, assembly language code, a higher-level language code, orthe like. Such code can include computer readable instructions forperforming various methods. The code may form portions of computerprogram products. Further, in an example, the code can be tangiblystored on one or more volatile, non-transitory, or non-volatile tangiblecomputer-readable media, such as during execution or at other times.Examples of these tangible computer-readable media can include, but arenot limited to, hard disks, removable magnetic disks, removable opticaldisks (e.g., compact disks and digital video disks), magnetic cassettes,memory cards or sticks, random access memories (RAMs), read onlymemories (ROMs), and the like.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherembodiments can be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is provided to complywith 37 C.F.R. § 1.72(b), to allow the reader to quickly ascertain thenature of the technical disclosure. It is submitted with theunderstanding that it will not be used to interpret or limit the scopeor meaning of the claims. Also, in the above Detailed Description,various features may be grouped together to streamline the disclosure.This should not be interpreted as intending that an unclaimed disclosedfeature is essential to any claim. Rather, inventive subject matter maylie in less than all features of a particular disclosed embodiment.Thus, the following claims are hereby incorporated into the DetailedDescription as examples or embodiments, with each claim standing on itsown as a separate embodiment, and it is contemplated that suchembodiments can be combined with each other in various combinations orpermutations. The scope of the invention should be determined withreference to the appended claims, along with the full scope ofequivalents to which such claims are entitled.

What is claimed is:
 1. A medical device, comprising: a patient statusinput unit configured to receive at least a first demographiccharacteristic indicator and a second demographic characteristicindicator of a patient, the second demographic characteristic indicatornon-identical to the first demographic characteristic indicator; asignal receiver circuit configured to receive one or more physiologicsignals of the patient; a signal metrics generator circuit, coupled tothe signal receiver circuit, configured to generate a plurality ofsignal metrics from the received one or more physiologic signals,including to generate a first set of one or more signal metrics from thereceived one or more physiologic signals according to a firstassociation with the first demographic characteristic indicator and inresponse to the first demographic characteristic indicator meeting atleast one first specified criterion, and to generate a second set ofsignal metrics from the received one or more physiologic signalsaccording to a second association with the second demographiccharacteristic indicator and in response to the second demographiccharacteristic indicator meeting at least one second specifiedcriterion; a patient-specific signal metric selector circuit configuredto select a subset of patient-specific signal metrics from the first andsecond sets of signal metrics using signal metric performance measuresof at least one metric from the first and second sets of signal metrics;and a target event or condition detector circuit configured to detect atarget heart failure event or condition of the patient when the selectedsubset of patient-specific signal metrics satisfies a detectionthreshold criterion, wherein the target event or condition detectorcircuit is further configured to generate an alert of the detectedtarget heart failure event.
 2. The device of claim 1, wherein the targetheart failure event or condition includes either an event indicative ofheart failure decompensation status of the patient, or an event ofworsening heart failure.
 3. The device of claim 1, wherein the patientstatus input unit is configured to receive a first chronic diseaseindicator and a second chronic disease indicator of the patient, whereinthe signal metrics generator circuit is configured to generate the firstset of signal metrics further based on the first chronic diseaseindicator, and to generate the second set of signal metrics furtherbased on the second chronic disease indicator.
 4. The device of claim 3,wherein the first chronic disease indicator includes a renal diseaseindicator, and the second chronic disease indicator includes a priorthoracic surgery indicator.
 5. The device of claim 1, wherein: thesignal receiver circuit is configured to be coupled to at least onephysiologic sensor configured to sense the one or more physiologicsignals from the patient; the signal metrics generator circuit comprisesa signal metrics performance analyzer circuit configured to generate,for the plurality of signal metrics, respective signal metricsperformance measures including at least one of a signal quality measure,a signal sensitivity measure, or a signal specificity measure; and thepatient-specific signal metrics selector circuit is configured to selectthe subset of patient-specific signal metrics using the respectivesignal metric performance measures.
 6. The device of claim 5, whereinthe signal metrics performance analyzer circuit is configured togenerate the signal sensitivity measure including a relative change ofthe respective signal metric from a first state to a second state, thesecond state including temporal information non-identical to that of thefirst state.
 7. The device of claim 6, wherein the signal metricsperformance analyzer circuit is configured to generated a sensitivity ofa daily maximum respiration rate (MRR) indicative of a relative changefrom a baseline daily MRR, and wherein the signal metrics generatorcircuit is configured to generate a set of signal metrics including thedaily MRR when the patient has no prior renal disease.
 8. The device ofclaim 1, wherein the patient-specific signal metrics selector circuit isconfigured to select, for the subset of patient-specific signal metrics,at least one signal metric from an intersection of the first set and thesecond set of signal metrics.
 9. The device of claim 1, wherein thepatient-specific signal metrics selector circuit is configured toselect, for the subset of patient-specific signal metrics, at least onesignal metric from the first set of signal metrics and at least onesignal metric from the second set of signal metrics.
 10. A method,comprising: receiving at least a first demographic characteristicindicator and a second demographic characteristic indicator of apatient, the second demographic characteristic indicator non-identicalto the first demographic characteristic indicator; sensing one or morephysiologic signals of the patient; generating a plurality of signalmetrics from the sensed one or more physiologic signals, includinggenerating a first set of one or more signal metrics according to afirst association with the first demographic characteristic indicatorand in response to the first demographic characteristic indicatormeeting at least one first specified criterion, and generating a secondset of signal metrics according to a second association with the seconddemographic characteristic indicator and in response to the seconddemographic characteristic indicator meeting at least one secondspecified criterion; selecting from the first and second sets of signalmetrics a subset of patient-specific signal metrics using signal metricperformance measures of at least one metric from the first and secondsets of signal metrics; applying a target event detection algorithm tothe selected patient-specific signal metric subset to detect a targetheart failure event or condition of the patient; and generating an alertof the detected target heart failure event.
 11. The method of claim 10,wherein applying a target event detection algorithm to detect a targetheart failure event or condition includes detecting either an eventindicative of heart failure decompensation status of the patient, or anevent of worsening heart failure.
 12. The method of claim 10, furthercomprising receiving a first chronic disease indicator and a secondchronic disease indicator of the patient, generating the first set ofsignal metrics further based on the first chronic disease indicator, andgenerating the second set of signal metrics further based on the secondchronic disease indicator.
 13. The method of claim 10, wherein:generating the plurality of signal metrics includes generating, for theplurality of signal metrics, respective signal metrics performancemeasures including at least one of a signal quality measure, a signalsensitivity measure, or a signal specificity measure; and selecting thesubset of patient-specific signal metrics includes using the respectivesignal metric performance measures.
 14. The method of claim 13, whereingenerating the signal sensitivity measure includes calculating arelative change of the respective signal metric from a first state to asecond state, the second state including temporal informationnon-identical to that of the first state.
 15. The method of claim 10,wherein selecting the subset of patient-specific signal metrics includesselecting at least one signal metric from an intersection of the firstset and the second set of signal metrics.
 16. The method of claim 10,wherein selecting the subset of patient-specific signal metrics includesselecting at least one signal metric from the first set of signalmetrics and at least one signal metric from the second set of signalmetrics.
 17. The method of claim 10, wherein applying the target eventdetection algorithm includes: generating a target event risk index usingthe selected subset of patient-specific signal metrics; determining adetection criterion associated with the target heart failure event orcondition using the at least one first or second demographiccharacteristic indicator; and detecting the target heart failure eventor condition when the target event risk index meets the detectioncriterion.
 18. The method of claim 17, wherein generating the targetevent risk index includes generating, for each signal metric of theselected subset of patient-specific signal metrics, a respective weightfactor, and calculating the target event risk index using a combinationof the subset of patient-specific signal metrics each being weighted bythe respective weight factor.